import requests

# url = 'http://10.191.77.181:40000/worker_get_status'
# controller_addr = "http://10.191.77.181:10000"
# message = "Tell me a story with more than 1000 words." 

# ret = requests.post(controller_addr + "/refresh_all_workers")
# ret = requests.post(controller_addr + "/list_models")
# models = ret.json()["models"]
# models.sort()
# print(f"Models: {models}")

import dataclasses
from enum import auto, Enum
from typing import List, Tuple
import base64
from io import BytesIO
from PIL import Image
import json

class SeparatorStyle(Enum):
    """Different separator style."""
    SINGLE = auto()
    TWO = auto()
    MPT = auto()
    PLAIN = auto()
    LLAMA_2 = auto()


@dataclasses.dataclass
class Conversation:
    """A class that keeps all conversation history."""
    system: str
    roles: List[str]
    messages: List[List[str]]
    offset: int
    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
    sep: str = "###"
    sep2: str = None
    version: str = "Unknown"

    skip_next: bool = False

    def get_prompt(self):
        messages = self.messages
        # 第一行作为模板，分为role和message两部分，将message中[1]位置的字符串中的<image>删除
        # 更新第一行，如果version为mmtag，更新第一行后在第一行前插入(self.roles[0], "<Image><image></Image>")和(self.roles[1], "Received.")
        # 否则，第一行的message前插入"<image>\n"
        if len(messages) > 0 and type(messages[0][1]) is tuple:
            messages = self.messages.copy()
            init_role, init_msg = messages[0].copy()
            init_msg = init_msg[0].replace("<image>", "").strip()
            if 'mmtag' in self.version:
                messages[0] = (init_role, init_msg)
                messages.insert(0, (self.roles[0], "<Image><image></Image>"))
                messages.insert(1, (self.roles[1], "Received."))
            else:
                messages[0] = (init_role, "<image>\n" + init_msg)

        if self.sep_style == SeparatorStyle.SINGLE:
            ret = self.system + self.sep
            for role, message in messages:
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    ret += role + ": " + message + self.sep
                else:
                    ret += role + ":"
        elif self.sep_style == SeparatorStyle.TWO:
            seps = [self.sep, self.sep2]
            ret = self.system + seps[0]
            for i, (role, message) in enumerate(messages):
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    ret += role + ": " + message + seps[i % 2]
                else:
                    ret += role + ":"
        elif self.sep_style == SeparatorStyle.MPT:
            ret = self.system + self.sep
            for role, message in messages:
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    ret += role + message + self.sep
                else:
                    ret += role
        elif self.sep_style == SeparatorStyle.LLAMA_2:
            wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n" if len(msg) > 0 else msg
            wrap_inst = lambda msg: f"[INST] {msg} [/INST]"
            ret = ""

            for i, (role, message) in enumerate(messages):
                if i == 0:
                    assert message, "first message should not be none"
                    assert role == self.roles[0], "first message should come from user"
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    if i == 0: message = wrap_sys(self.system) + message
                    if i % 2 == 0:
                        message = wrap_inst(message)
                        ret += self.sep + message
                    else:
                        ret += " " + message + " " + self.sep2
                else:
                    ret += ""
            ret = ret.lstrip(self.sep)
        elif self.sep_style == SeparatorStyle.PLAIN:
            seps = [self.sep, self.sep2]
            ret = self.system
            for i, (role, message) in enumerate(messages):
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    ret += message + seps[i % 2]
                else:
                    ret += ""
        else:
            raise ValueError(f"Invalid style: {self.sep_style}")

        return ret

    def append_message(self, role, message):
        self.messages.append([role, message])

    def process_image(self, image, image_process_mode, return_pil=False, image_format='JPG', max_len=1344, min_len=672):
        if image_process_mode == "Pad":
            def expand2square(pil_img, background_color=(122, 116, 104)):
                width, height = pil_img.size
                if width == height:
                    return pil_img
                elif width > height:
                    result = Image.new(pil_img.mode, (width, width), background_color)
                    result.paste(pil_img, (0, (width - height) // 2))
                    return result
                else:
                    result = Image.new(pil_img.mode, (height, height), background_color)
                    result.paste(pil_img, ((height - width) // 2, 0))
                    return result
            image = expand2square(image)
        elif image_process_mode in ["Default", "Crop"]:
            pass
        elif image_process_mode == "Resize":
            image = image.resize((336, 336))
        else:
            raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
        if max(image.size) > max_len:
            max_hw, min_hw = max(image.size), min(image.size)
            aspect_ratio = max_hw / min_hw
            shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
            longest_edge = int(shortest_edge * aspect_ratio)
            W, H = image.size
            if H > W:
                H, W = longest_edge, shortest_edge
            else:
                H, W = shortest_edge, longest_edge
            image = image.resize((W, H))
        if return_pil:
            return image
        else:
            buffered = BytesIO()
            image.save(buffered, format=image_format)
            img_b64_str = base64.b64encode(buffered.getvalue()).decode()
            return img_b64_str

    def get_images(self, return_pil=False):
        images = []
        for i, (role, msg) in enumerate(self.messages[self.offset:]):
            if i % 2 == 0:
                if type(msg) is tuple:
                    import base64
                    from io import BytesIO
                    from PIL import Image
                    msg, path_to_image, image_process_mode = msg
                    image = Image.open(path_to_image)
                    if image.mode == 'RGBA':
                        image = image.convert('RGB')
                    if image_process_mode == "Pad":
                        def expand2square(pil_img, background_color=(122, 116, 104)):
                            width, height = pil_img.size
                            if width == height:
                                return pil_img
                            elif width > height:
                                result = Image.new(pil_img.mode, (width, width), background_color)
                                result.paste(pil_img, (0, (width - height) // 2))
                                return result
                            else:
                                result = Image.new(pil_img.mode, (height, height), background_color)
                                result.paste(pil_img, ((height - width) // 2, 0))
                                return result
                        image = expand2square(image)
                    elif image_process_mode in ["Default", "Crop"]:
                        pass
                    elif image_process_mode == "Resize":
                        image = image.resize((336, 336))
                    else:
                        raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
                    max_hw, min_hw = max(image.size), min(image.size)
                    aspect_ratio = max_hw / min_hw
                    max_len, min_len = 800, 400
                    shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
                    longest_edge = int(shortest_edge * aspect_ratio)
                    W, H = image.size
                    if longest_edge != max(image.size):
                        if H > W:
                            H, W = longest_edge, shortest_edge
                        else:
                            H, W = shortest_edge, longest_edge
                        image = image.resize((W, H))
                    if return_pil:
                        images.append(image)
                    else:
                        buffered = BytesIO()
                        image.save(buffered, format="PNG")
                        img_b64_str = base64.b64encode(buffered.getvalue()).decode()
                        images.append(img_b64_str)
        return images

    def to_gradio_chatbot(self):
        ret = []
        for i, (role, msg) in enumerate(self.messages[self.offset:]):
            if i % 2 == 0:
                if type(msg) is tuple:
                    msg, image, image_process_mode = msg
                    img_b64_str = self.process_image(
                        image, "Default", return_pil=False,
                        image_format='JPEG')
                    img_str = f'<img src="data:image/jpeg;base64,{img_b64_str}" alt="user upload image" />'
                    msg = img_str + msg.replace('<image>', '').strip()
                    ret.append([msg, None])
                else:
                    ret.append([msg, None])
            else:
                ret[-1][-1] = msg
        return ret

    def copy(self):
        return Conversation(
            system=self.system,
            roles=self.roles,
            messages=[[x, y] for x, y in self.messages],
            offset=self.offset,
            sep_style=self.sep_style,
            sep=self.sep,
            sep2=self.sep2,
            version=self.version)

    def dict(self):
        if len(self.get_images()) > 0:
            return {
                "system": self.system,
                "roles": self.roles,
                "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages],
                "offset": self.offset,
                "sep": self.sep,
                "sep2": self.sep2,
            }
        return {
            "system": self.system,
            "roles": self.roles,
            "messages": self.messages,
            "offset": self.offset,
            "sep": self.sep,
            "sep2": self.sep2,
        }

def build_conversation(conv):
    conv_vicuna_v0 = Conversation(
        system="A chat between a curious human and an artificial intelligence assistant. "
            "The assistant gives helpful, detailed, and polite answers to the human's questions.",
        roles=("Human", "Assistant"),
        messages=(
            ("Human", "What are the key differences between renewable and non-renewable energy sources?"),
            ("Assistant",
                "Renewable energy sources are those that can be replenished naturally in a relatively "
                "short amount of time, such as solar, wind, hydro, geothermal, and biomass. "
                "Non-renewable energy sources, on the other hand, are finite and will eventually be "
                "depleted, such as coal, oil, and natural gas. Here are some key differences between "
                "renewable and non-renewable energy sources:\n"
                "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable "
                "energy sources are finite and will eventually run out.\n"
                "2. Environmental impact: Renewable energy sources have a much lower environmental impact "
                "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, "
                "and other negative effects.\n"
                "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically "
                "have lower operational costs than non-renewable sources.\n"
                "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote "
                "locations than non-renewable sources.\n"
                "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different "
                "situations and needs, while non-renewable sources are more rigid and inflexible.\n"
                "6. Sustainability: Renewable energy sources are more sustainable over the long term, while "
                "non-renewable sources are not, and their depletion can lead to economic and social instability.\n")
        ),
        offset=2,
        sep_style=SeparatorStyle.SINGLE,
        sep="###",
    )

    conv_vicuna_v1 = Conversation(
        system="A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions.",
        roles=("USER", "ASSISTANT"),
        version="v1",
        messages=(),
        offset=0,
        sep_style=SeparatorStyle.TWO,
        sep=" ",
        sep2="</s>",
    )

    conv_llama_2 = Conversation(
        system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.

    If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""",
        roles=("USER", "ASSISTANT"),
        version="llama_v2",
        messages=(),
        offset=0,
        sep_style=SeparatorStyle.LLAMA_2,
        sep="<s>",
        sep2="</s>",
    )

    conv_llava_llama_2 = Conversation(
        system="You are a helpful language and vision assistant. "
            "You are able to understand the visual content that the user provides, "
            "and assist the user with a variety of tasks using natural language.",
        roles=("USER", "ASSISTANT"),
        version="llama_v2",
        messages=(),
        offset=0,
        sep_style=SeparatorStyle.LLAMA_2,
        sep="<s>",
        sep2="</s>",
    )

    conv_mpt = Conversation(
        system="""<|im_start|>system
    A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
        roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
        version="mpt",
        messages=(),
        offset=0,
        sep_style=SeparatorStyle.MPT,
        sep="<|im_end|>",
    )

    conv_llava_plain = Conversation(
        system="A chat between a curious human and an artificial intelligence assistant.",
        roles=("Human", "Assistant"),
        messages=(
        ),
        offset=0,
        sep_style=SeparatorStyle.PLAIN,
        sep="\n",
    )

    conv_clean = Conversation(
        system="",
        roles=("Human", "Assistant"),
        messages=(
        ),
        offset=0,
        sep_style=SeparatorStyle.PLAIN,
        sep="\n",
    )

    conv_llava_v0 = Conversation(
        system="A chat between a curious human and an artificial intelligence assistant. "
            "The assistant gives helpful, detailed, and polite answers to the human's questions.",
        roles=("Human", "Assistant"),
        messages=(
        ),
        offset=0,
        sep_style=SeparatorStyle.SINGLE,
        sep="###",
    )

    conv_llava_v0_mmtag = Conversation(
        system="A chat between a curious user and an artificial intelligence assistant. "
            "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
            "The visual content will be provided with the following format: <Image>visual content</Image>.",
        roles=("Human", "Assistant"),
        messages=(
        ),
        offset=0,
        sep_style=SeparatorStyle.SINGLE,
        sep="###",
        version="v0_mmtag",
    )

    conv_llava_v1 = Conversation(
        system="A chat between a curious human and an artificial intelligence assistant. "
            "The assistant gives helpful, detailed, and polite answers to the human's questions.",
        roles=("USER", "ASSISTANT"),
        version="v1",
        messages=(),
        offset=0,
        sep_style=SeparatorStyle.TWO,
        sep=" ",
        sep2="</s>",
    )

    conv_llava_v1_mmtag = Conversation(
        system="A chat between a curious user and an artificial intelligence assistant. "
            "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
            "The visual content will be provided with the following format: <Image>visual content</Image>.",
        roles=("USER", "ASSISTANT"),
        messages=(),
        offset=0,
        sep_style=SeparatorStyle.TWO,
        sep=" ",
        sep2="</s>",
        version="v1_mmtag",
    )

    conv_mistral_instruct = Conversation(
        system="",
        roles=("USER", "ASSISTANT"),
        version="llama_v2",
        messages=(),
        offset=0,
        sep_style=SeparatorStyle.LLAMA_2,
        sep="",
        sep2="</s>",
    )

    conv_chatml_direct = Conversation(
        system="""<|im_start|>system
    Answer the questions.""",
        roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
        version="mpt",
        messages=(),
        offset=0,
        sep_style=SeparatorStyle.MPT,
        sep="<|im_end|>",
    ),


    conv_templates = {
        "default": conv_vicuna_v0,
        "v0": conv_vicuna_v0,
        "v1": conv_vicuna_v1,
        "vicuna_v1": conv_vicuna_v1,
        "llama_2": conv_llama_2,
        "mistral_instruct": conv_mistral_instruct,
        "chatml_direct": conv_chatml_direct,
        "mistral_direct": conv_chatml_direct,

        "plain": conv_llava_plain,
        "v0_plain": conv_llava_plain,
        "llava_v0": conv_llava_v0,
        "v0_mmtag": conv_llava_v0_mmtag,
        "llava_v1": conv_llava_v1,
        "v1_mmtag": conv_llava_v1_mmtag,
        "llava_llama_2": conv_llava_llama_2,

        "mpt": conv_mpt,
    }

    return eval(conv)




# message = "List as much instance in the picture as you can in nouns without modifier as the following example:\n\
# noun1. noun2. noun3.\n\
# Every noun consists of no more than three words. Separate nouns with periods. Do not repeat same instances."


def ask_once(path_to_image, message, conversation):
    if path_to_image is not None:
        text = message  # Hard cut-off for images
        if '<image>' not in text:
            # text = '<Image><image></Image>' + text
            text = text + '\n<image>'
        text = (text, path_to_image, "Default")
        state = conversation.copy()
    state.append_message(state.roles[0], text)
    state.append_message(state.roles[1], None)
    state.skip_next = False

    prompt = state.get_prompt()

    headers = {"User-Agent": "LLaVA Client"}


    pload = {
        "model": "llava-v1.5-13b",
        "prompt": prompt,
        "max_new_tokens": 800,
        "temperature": 0.1,
        "images": state.get_images(),
        "stop": state.sep,
    }

    response = requests.post("http://10.191.77.181:10000/worker_generate_stream", headers=headers,
            json=pload, stream=True)

    # print(response)
    # print(prompt.replace(conv.sep, "\n"), end="")


    # *_, last = response 

    # iter = 0
    # for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b"\0"):
    #     iter += 1
    #     if chunk:
    #         if iter > 50 and chunk[-1] == '.':
    #             break
    #         data = json.loads(chunk.decode("utf-8"))
    #         output = data["text"].split(state.sep)[-1]

    for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b"\0"):
        if chunk:
            data = json.loads(chunk.decode("utf-8"))
            output = data["text"].split(state.sep)[-1]

    return output

def postprocess(s):
    filter_set = set()
    s = s.strip('ASSISTANT:')
    for i in s.split('.'):
        if len(i) > 0:
            filter_set.add(i + ' . ')
    output = ''.join(list(filter_set))
    return output

import os
import json
from tqdm import tqdm

if __name__ == '__main__':
    
    default_conversation = build_conversation('conv_vicuna_v1')

    work_path = "/home/oyl1sgh/llava_stuffs/OpenLane-V2"

    results = ""

    for scene in range(10000, 10150):
        print('------------------------')
        print(f'Processing scene {scene}...')
        print('------------------------')
        scene = str(scene)
        scene_path = os.path.join(work_path, scene)
        imgs = os.listdir(scene_path)
        if len(imgs) == 0:
            line = scene + '\n'
            print(line)
            results += line
            continue
        for img in imgs:
            img_name = img.split('.png')[0]
            img_path = os.path.join(scene_path, img)
            prompt = f'''
You are an expert in determining positional relationships of lane segments in the image. Is the green segment on the left of the blue segment, or on the right? Please reply in a brief sentence.
        '''
            result = ask_once(img_path, prompt, default_conversation).lower()
            print(result)
            if "left" in result and "right" not in result:
                label = '1'
            elif "right" in result and "left" not in result:
                label = '2'
            else:
                label = '0'
            line = scene + " " + img_name + " " + label + "\n"
            # line = scene + " " + img_name + " " + result + "\n"
            print(line)
            results += line

    with open("/home/oyl1sgh/llava_stuffs/llava_VQA_result.txt", "w") as f:
        f.write(results)

